ABSTRACT
Covid19 has devastated all continents causing disasters not only on the health sector but also at social, economic, and at political levels. The world is still trying to eradicate the virus. One of the measures taken is to inform citizens about the virus in order to avoid contamination as much as possible. Several people lost their jobs, and found themselves without any income. The whole world is confined, and the poor can no longer endure this critical situation. Financial assistance is therefore necessary in order to reduce the impact. This paper aims to propose an intelligent financial support application that computes the eligibility for a citizen to get a support during the pandemic;and to explain steps for chatbot using DialogFlow. The training realized using a machine learning algorithm was chosen after making a comparison between some other algorithms. Gradient Boosting Classifier algorithm was the accurate and most efficient for the application. It is possible to train the system again using other data set to make any adaptive results or computations. Copyright © (2022) by International Association for Development of the Information Society (IADIS). All rights reserved.
ABSTRACT
The COVID-19 increased the importance of patient's continuous assessment of health outcomes. In 2021 WHO proposed some Digital Health guidelines arguing that health systems should consider the use of emergent technologies in health care services. This health environment is providing intelligent systems to guide patients in self-care. One example of that is the chatbot which is, a conversational agent that have been assuming an important role in how to improve health knowledge, reducing the incidence of diseases and avoiding new ones. Pregnant women are a profile where the self-care referred before is a critical issue. Prenatal services reveal to be an important part of the care process where most complications for that women happen. This article aims to comprehend how pregnant women interact with a conversational agent and how relevant this Digital Health tool is for primary health care services. The study presents the process and results of a systematic literature review about the user experience with of a chatbot in pregnant women self-care context; a summary of GISSA intelligent chatbot development including the use of technologies such as DialogFlow; and the process and results of GISSA usability evaluation in research field. Results show that a small amount of articles was gathered and the chatbot as a tool is a relevant opportunity for primary care health services in Brasil.
ABSTRACT
Chatbots or conversational agents' tools have become important means of communication, especially with the needs arising from COVID-19 and the increase in artificial intelligence solutions. This work presents a case study of the use of a chatbot, specifically the WhatsApp messaging application, to assist in the pre-triage sector of a university dental clinic. To this end, several chatbot solutions were compared and the DialogFlow tool was chosen, which employs Machine Learning techniques to create the chatbot. The Twillio cloud communication platform was used to make the connection with the social network. The PSSUQ usability questionnaire (15-people sample space), was used to assess the satisfaction of chatbot users, analyze its responsiveness and possible failures, as well as verify if the bot achieves its initial objective and analyze its behavior. © 2022 IEEE.
ABSTRACT
A chatbot is a software that establishes a human–machine interaction by using natural language processing (NLP) and natural language understanding (NLU). One of the essential tasks in machine learning and artificial intelligence is the mechanism through which conversation between a bot and a human is built and modelled. On the other hand, mental health issues have emerged up as one of the most rising diseases affecting millions around the world and are still one of the most untreated ones. People suffering from mental health disorders and issues have to live with it every single day, and the cases rose prominently during the COVID-19 pandemic. In this paper, a machine learning and natural language processing-based chatbot has been implemented to hold conversations with the purpose of mental healthcare assistance. It has been named MOODY;an intelligent conversational agent built with Dialogflow integration on NLP, NLU and its sentiment analysis feature. Dialogflow's architecture has been taken from the end-to-end neural network machine translation domain. There are plenty of variations and features that have been introduced resulting in remarkably enhancing the conversational capabilities of chatbots. MOODY's dependency, quick response generation and privacy provide a unique opportunity to assist those in need of adequate information and resources at one place. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
The coronavirus has prevented face-to-face consultations of procedures in the entities, so Ecuadorian companies have opted to use technological tools to provide smooth service to their customers. Among them is the Chatbot, which allows users to receive personalized attention through a computer. This paper presents the development of a chatbot for the Prefecture of Los Rios in Ecuador. The main objective is to present the development of a chatbot for the attention and management of services offered to citizens of the Prefecture of Los Rios. Direct research for collecting the necessary information and a qualitative approach study for the development of this project involved interviews in learning about the needs and requirements of citizens. The Chatbot Prefectura Los Ríos uses frameworks for natural language processing developed from a Dialog flow tool, resulting in users' acceptance. © 2022 IEEE Computer Society. All rights reserved.